Abstract
Accurately and automatically recognizing the emotional states of human beings is the central task in affective computing. The electroencephalography (EEG) data, generated from the neural activities in brain cortex, provide us with a reliable data source to perform emotion recognition. Besides the recognition accuracy, it is also necessary to explore the importance of different EEG features, frequency bands, and channels in emotion expression. In this article, we propose a unified framework termed graph-regularized least square regression with feature importance learning (GFIL) to simultaneously achieve these goals by incorporating an autoweighting variable into the least square regression. Unlike the widely used trial-and-error manner, GFIL automatically completes the identification once it is trained. Specifically, GFIL can: 1) adaptively discriminate the contributions of different feature dimensions; 2) automatically identify the critical frequency bands and channels; and 3) quantitatively rank and select the features by the learned autoweighting variable. From the experimental results on the SEED_IV data set, we find GFIL obtained improved accuracies based on the feature autoweighting strategy, which are 75.33%, 75.03%, and 79.17% corresponding to the three cross-session recognition tasks (session1->session2, session1->session3, session2->session3), respectively. Additionally, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gamma</i> band is identified as the most important one and the channels locating in the prefrontal and left/right central regions are more important.
Published Version
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